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Meta Software Engineer - AI/HPC Systems Specialist in Oslo, Norway

Summary:

Support the development of Meta’s on-house silicon AI accelerators by developing collective communications software library and optimizing distributed AI/ML workloads’ performance.

Required Skills:

Software Engineer - AI/HPC Systems Specialist Responsibilities:

  1. Contribute to our developer infrastructure, including simulation and HW emulation platforms, to enable performance measurement and optimization for Meta’s in-house accelerator programs.

  2. Understand and contribute to the collective communications library, intended to be deployed on Meta’s AI/ML superclusters.

  3. Support networking and compute hardware acceleration techniques to improve ML inference and training model performance.

  4. Perform architectural analysis to ensure system designs meet performance, scalability, and reliability requirements.

  5. Implement simulation models for Meta’s Accelerator ASICs, develop and analyze various scenarios to evaluate data center performance and identify potential improvements.

  6. Collaborate with architects and engineers to integrate simulation results into system design processes.

  7. Use instruction set simulators to define performant firmware for Meta's training/inference accelerators.

  8. Collaborate with hardware and firmware teams to ensure accurate modeling and simulation of accelerator functionalities.

  9. Analyze simulation results to guide firmware development and optimization efforts.

Minimum Qualifications:

Minimum Qualifications:

  1. Bachelor or higher in Computer Science, Computer Engineering, or any other relevant technical field

  2. 5+ years experience in developing C++ codebase

  3. 5+ years experience in developing Python codebase

  4. Understanding of performance and benchmarking measurement and optimization on collective communications and distributed at-scale model training

Preferred Qualifications:

Preferred Qualifications:

  1. Full-stack experience and understanding of AI/HPC systems, from HW/infrastructure through the application layer, performance optimizations, including familiarity with relevant tools, libraries, and frameworks (e.g., NCCL, PyTorch, CUDA)

  2. Experience in one or more of the following machine learning/deep learning domains: hardware accelerators, AI Infrastructure, and/or high performance computing (HPC), particularly pertaining to interconnect and collective

  3. Knowledge of AI/HPC hardware requirements and specifications (e.g., configuring hardware components, GPU, memory, network for AI/HPC workloads)

  4. Understanding of the transport stack (e.g., RoCE), its constraints and performance measures and how transport considerations enable the collective communications stack

Industry: Internet

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